1. A combined microwave and infrared radiometer approach for a high resolution global precipitation map in the GSMaP Japan Tomoo Ushio, K. Okamoto, K. Aonashi, T. Inoue, T. Kubota, H. Hashizume, T. Simomura, T. Iguchi, N. Takahashi, R. Oki, M. Kachi

2. Outline Background  On the GSMaP project  Microwave radiometer based precipitation map  Needs for the Infrared data (IR) Methodology  Cloud motion and Kalman filter approach from the Geo-IR data Results  Demonstration of our product  Initial evaluation of our product  PEHRPP activities in Japan Summary and future directions

3. Goals of the project  Production of high precision and high resolution global precipitation map by using satelliteborne microwave radiometer data -e.g. Spatial resolution: 0.1 ﾟ✕ 0.1 ﾟ, Temporal resolution: 1 day -Microwave radiometers (TMI, SSM/I ×3, AMSR-E, AMSR) -Precipitation radar, IR data  Development of reliable microwave radiometer algorithm -Based on the common physical precipitation model which precipitation radar also uses. Even in version 6 TRMM algorithms, about 10-15% discrepancy can be seen in monthly average rainfall rates retrieved by TMI and PR.  Establishment of precipitation map production technique by using multi-satellite data for the coming GPM era

4. Obs. Data Precip. Retrieval Flow of the GSMaP Project Routine Obs. Campaign Obs. Data base Parameter Sensitivity Exp. Match-up Data Anal. Look-up Table Verify Precip. Map Products Obs. Data High Temporal Resolution Map Obs. Data Radar Algorithm Meteor. Satellites Global Precip. Map TRMM/PR Ground Obs. Precip. Map Data base Precip. Physical Model Algorithm Ｇ. Precip. Physical Model G. Ground Radar Obs. G. Global Precip. Map Ｇ. Algorithm Microwave Radiometer Interpolation Algo.

5. How do we get a global precipitation map? The accurate estimation of surface rainfall on a global scale with high resolution has been one of the major goals in global water cycle and its related area. Ground based approach  Fairly good estimation  Generally suffer from spatial coverage problems. Satellite based approach  Fairly good coverage and reasonably good estimation  There is not a single space-born sensor to detect surface rainfall in near real time on a global basis.  We need to combine the data from multiple satellites.

6. Approach of the GSMaP project We use the Aonashi Algorithm to retrieve rainfall rate. The sensors for the analysis are TMI, AMSR-E, AMSR, SSMI (F13, 14, 15). NameData available TRMM (TMI)Jan. 1998 to Dec. 2005 Aqua (AMSR-E)Jan. 2003 to Oct. 2005 ADEOS-II (AMSR)Apr. 2003 to Oct. 2003 DMSP (SSMI:F13, F14, F15)Sep. 2003, July. 2005 and several

10. Monthly precipitation accumulation from DMSP/SSMI (F13, 14, 15) for Sep. 2003 F13 F15 F14

11. 6 hourly MWR combined map Combined 6 hourly TMI AMSR & AMSR-E SSM/I （ F13, F14, F15 ）

14. How can we get a global precipitation map with temporal resolution of 3 hours or less? Infrared radiometers (IR)  can provide information on cloud top layers (not precipitation)  Can ensure a global coverage with high temporal resolution (> 30 min) due to the geo-synchronous orbit (GEO) Microwave radiometers (MW)  Can detect cloud structure and precipitation with high spatial resolution  The major draw back is temporal sampling due low earth orbit satellite (LEO) The LEO-MW and GEO-IR radiometry are quite complementary for monitoring the highly variable parameters like precipitation.

15. How do we combine the MWR and IR data? Combination of the moving vector and Kalman filtering method The moving vector method was introduced by Joyce et al. [2004].  Joyce R., J. Janowiak, P. Arkin, and P. Xie, CMORPH: A method that produces global precipitation estimates from passive microwave and ifrared data at high spatial and temporal resolution, J. Hydrometeorology, 487-503, 2004  Advantage MWR based approach (not Tb but cloud motion) Fast processing time  Disadvantage Not include the developing and decaying process of precipitation Kalman filter approach  Refine precipitation rate on Kalman gain after propagating the rain pixel  The Kalman gain is determined from the database on the relationship between the IR Tb and surface rain rate. New!!

16. 11.4 μm Geo IR 1 hour before 11.4 μm Geo IR Present Infrared (IR) Data Microwave Radiometer (MWR) Data 1 hr Moving Vector GSMaP Data GSMaP Present GSMaP 1 hour before 1 hr MWR Present Algorithm flow Predicted GSMaP Kalman Filter

17. State and observation equation used in Kalman filter ： Rain rate at time k ： Infrared Tb ： Rain rate at time k+1 ： System noise ： Observation noise

18. Predicted rain rate IR Tb Predicted rain rate Refinement Prediction GSMaP Kalman Filter Obs. Noise System Noise

19. Correlation between radar and the GSMaP product as a function of the past microwave satellite overpass With Kalman filter Without Kalman filter Moving vector only

20. Effect of Kalman Filter （ Aug. 2000 ） ‐ TRMM/TMI only ‐ Correlation Time （ｈｒ） GSMaP ＶＳ Radar rain gauge network in Japan ■ ： with Kalman filter ▲ ： Moving vector only

24. On the PEHRPP web in Japan We started to make the evaluation web site using the radar-rain gauge network data around Japan in 2005. The IDL codes to make the web are all from Dr. Beth Ebert.

27. A comparison of the GSMaP with CMORPH from the PEHRPP web in Japan

28. PEHRPP web site in Japan http://www.radar.aero.osakafu- u.ac.jp/~gsmap/IPWG/dailyval.html http://www.radar.aero.osakafu- u.ac.jp/~gsmap/IPWG/dailyval.html Or you can access this site by clicking the address on the DVD.

29. Summary Initial results of the global precipitation map from the MWR and from IR and MWR combined algorithm were introduced and demonstrated. The details of the GSMaP project are in the DVD I brought.

30. Acknowledgements Thanks to Dr. Bob Adler and Kris Kummerow, we could kick off this project. Thanks to Dr. Beth Ebert and Dr. Phil Arkin, we could make the web site.

31. Thank you!! 謝謝 !! Danke!! Merci!! ありがとう！！

32. Global Satellite Mapping of Precipitation project Organization of Research Team in FY 2005 Principal Investigator K. Okamoto Administrative Assistant K. Matsukawa Ground Radar Observation Group K. Iwanami ( Leader ) K. Nakagawa ， H. Hanado ， K. Kitamura Precipitation Physical Model Production Group N. Takahashi ( Leader ), J.Awaka, T. Kozu ， S. Satoh ， Y. Takayabu ， M.Hirose Algorithm Developing Group T. Iguchi ( Leader ) ， M. Fujita ， T. Inoue, K. Aonashi ， S. Shimizu, S.Seto, H.Eito, K.Takahashi Satellite Data Processing and Global Map Production Group T. Ushio ( Leader ) ， S. Shige, H.Hashizume, R. Oki ， M. Kachi, T. Kubota, Y. Iida, H.Sasaki

33. What, When, Where, and How do we analyze for? Purpose:To map the global precipitation map with 0.1 degree/1 hour resolution What:IR: 1hour global IR data from Goddard/DAAC MWR: TMI, AMSR-E, AMSR, and SSM/I×3 When:July 2005 Where: 60 degree in latitude around globe How:By interpolating precipitation between MWR overpasses using the cloud motion and Kalman filtering inferred from 1 hour IR images.